Papers with adaptive policies
Language Model Augmented Monotonic Attention for Simultaneous Translation (2022.naacl-main)
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| Challenge: | Existing adaptive policies for simultaneous neural machine translation use monotonic attention to perform read/write decisions based on the partial source and target sequences. |
| Approach: | They propose a framework to aid monotonic attention with an external language model to improve its decisions. |
| Outcome: | The proposed approach improves on English-German and English-French translation tasks by using a language model. |
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
Simultaneous Machine Translation with Tailored Reference (2023.findings-emnlp)
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| Challenge: | Existing SiMT models are trained using the same reference disregarding the varying amounts of available source information at different latency. |
| Approach: | They propose a method that provides tailored reference for the SiMT models trained at different latency by rephrasing ground-truth to the tailored reference. |
| Outcome: | The proposed method achieves state-of-the-art translation performance on three translation tasks. |
Simultaneous Translation Policies: From Fixed to Adaptive (2020.acl-main)
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| Challenge: | Adaptive policies can balance translation quality and latency based on context information . previous methods on obtaining adaptive policies rely on complicated training process . |
| Approach: | They propose to obtain adaptive policies by a simple heuristic composition of fixed policies . they propose to use a heurism to obtain policies that can outperform fixed ones . |
| Outcome: | Experiments on Chinese -> English and German -> english show that adaptive policies outperform fixed policies by up to 4 BLEU points for the same latency. |
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path. |
| Approach: | They propose a multimodal approach to simultaneous machine translation using reinforcement learning with strategies to integrate visual and textual information in both the agent and the environment. |
| Outcome: | The proposed multimodal approach improves translation quality while keeping latency low while providing visual cues. |
Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)
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| Challenge: | Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies . |
| Approach: | They propose a more flexible approach by decoupling the adaptive policy model from the translation model. |
| Outcome: | The proposed approach outperforms baseline approaches in translation tasks. |
DrFrattn: Directly Learn Adaptive Policy from Attention for Simultaneous Machine Translation (2025.emnlp-main)
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| Challenge: | Existing approaches to learn read/write policies from attention mechanism may compromise effectiveness of attention mechanism . |
| Approach: | They propose a method that directly learns adaptive policies from the attention mechanism . experimental results demonstrate that the method achieves an improved balance between translation accuracy and latency. |
| Outcome: | The proposed method achieves improved balance between translation accuracy and latency. |
Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning (2026.acl-long)
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| Challenge: | Prior approaches to rewriting large language models shatters linguistic coherence and removes privacy-sensitive information. |
| Approach: | They propose a framework that trains an agent to dynamically route text chunks . it implicitly distinguishes between replaceable Personally Identifiable Information (PII) and task-critical PII . |
| Outcome: | The proposed framework achieves state-of-the-art on the privacy-utility frontier . it trains an agent to dynamically route text chunks, learning a policy that balances privacy leakage and task performance. |